Master of Science in Computer Science and Engineering - Artificial Intelligence (Rational Decision Making)
国际学生入学条件
We expect applicants to have earned their bachelor’s degrees by the time they matriculate, and to possess strong backgrounds in computer science or a related discipline. To be eligible for the M.S. program, students must not already hold a master’s degree in computer science or an equivalent field.
Successful MS applicants usually have an undergraduate GPA of at least 3.5/4.0 and three strong letters of recommendation. GRE scores are neither required nor expected. International students must demonstrate English proficiency. TOEFL ibt: 84, IELTS: 6.5, toefl Paper 560, Statement of Purpose, Personal Statement Resume: Please provide your resume that highlights your education, work experience, research publications, teaching experience, professional activities, volunteer service, community engagement, honors and awards.
Research Interest (PhD applicants only): Select your primary and secondary areas of research interest.
Faculty Interest (PhD applicants only): Select up to five CSE faculty whose research interests align with yours and whom you would consider as potential research advisors.
Letters of Recommendation: Request 3 letters of recommendation from people who are familiar with your academic performance and research potential.
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雅思考试总分
6.5
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- 雅思总分:6.5
- 托福网考总分:84
- 托福笔试总分:560
- 其他语言考试:MET - 4-Skill test; minimum Overall score = 59
课程简介
The master's degree in CSE is primarily intended for students desiring to substantially advance their knowledge and skill in a field or fields of CSE. The relatively small investment in time to get a master's degree will lead to greater professional opportunities and significantly higher salaries. The Artificial Intelligence (AI) program at the University of Michigan comprises a multidisciplinary group of researchers conducting theoretical, experimental, and applied investigations of intelligent systems. Current projects include research in rational decision making, distributed systems of multiple agents, machine learning, reinforcement learning, cognitive modeling, game theory, natural language processing, machine perception, healthcare computing, and robotics. Research in the Artificial Intelligence laboratory tends to be highly interdisciplinary, building on ideas from computer science, linguistics, psychology, economics, biology, controls, statistics, and philosophy. In pursuing this approach, laboratory faculty and students work closely with colleagues throughout the University. This collaborative environment, coupled with our diverse perspectives, leads to a valuable interchange of ideas within and across research groups.
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